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Hydraulic-Hydrologic Model for the Zambezi River Using Satellite Data and Artificial Intelligence Techniques

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Hydraulic-Hydrologic Model for the Zambezi River Using Satellite Data and Artificial Intelligence Techniques Hydraulic-hydrologic model for the Zambezi River using satellite data and artificial intelligence techniques THÈSE NO 6225 (2014) PRÉSENTÉE LE 11 JUILLET 2014 À L’ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE À LA FACULTÉ DE L'ENVIRONNEMENT NATUREL, ARCHITECTURAL ET CONSTRUIT LABORATOIRE DE CONSTRUCTIONS HYDRAULIQUES ET À L’INSTITUTO SUPERIOR TÉCNICO (IST) DA UNIVERSIDADE DE LISBOA DEPARTAMENTO DE ENGENHARIA CIVIL, ARQUITECTURA E GEORRECURSOS (DECIVIL) PROGRAMME DOCTORAL EN ENVIRONNEMENT ET DOUTORAMENTO EM ENGENHARIA CIVIL POUR L’OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCES (PhD) PAR José Pedro GAMITO DE SALDANHA CALADO MATOS acceptée sur proposition du jury: Prof. J.-L. Scartezzini, président du jury Prof. A. Schleiss, Prof. M. M. Portela Correia dos Santos Ramos da Silva, directeurs de thèse Dr J.-M. Fallot, rapporteur Dr F. Jordan, rapporteur Dr B. Schaefli, rapporteuse Suisse 2014 Man searches for answers and finds only questions Unknown author Abstract ABSTRACT The Zambezi River Basin (ZRB), in Africa, spreads over some unfathomable 1 370 000 km2. In all its magnificence it is home to approximately 30 million inhabitants, harbors a number of priceless wildlife sites and has an estimated hydropower production capacity of 13 000 MW, of which only about 5 000 MW are currently exploited. The Zambezi River is central to both the culture and the economy of riparian countries. With a heterogeneous landscape and a semi-arid climate, the basin faces great challenges brought about by growing populations, soaring economic growth, and climate change. In the future, increasing pressure on water resources is inescapable. Hydrological modeling will certainly support decision makers in all levels of decision as they rise to meet the forthcoming challenges. While historically the basin’s size, heterogeneity, political situation, and constraining lack of hydrological data have conditioned the scope and success of hydrological models of the basin, relatively new technologies such as satellite remote sensing or machine learning present promising tools with which some of these problems can be addressed. The present work set out to develop a performing hydraulic-hydrological model of the ZRB at a daily time scale, envisaging future use in dam operation optimization and synchronization, environmental impact assessments, evaluation of future scenarios (predicting responses to climate change and increased demands) and a broad range of other studies related to themes such as wildlife, water chemistry, sediment transport, and integrated water management. In order for this to be successful, constraining issues, mostly related to input data, would have to be addressed before the actual modeling stages; resorting to satellite remote sensed data would be mandatory and the most had to be made out of the few good quality discharge series available. Also, it was early recognized that no single model could be a “best” choice for such a wide array of uses and that a large emphasis would have to be placed on model calibration and validation. Aiming to extend the time scope of the analysis, the novel Pattern-Oriented Memory (POM) historical rainfall interpolation methodology was introduced. Based on machine learning models, POM was shown to be superior to competing methods in data scarce environments and when the true rainfall field shows high variability. Over the ZRB, errors in the POM interpolated rainfall series were observed to be on par with those of state-of-the-art satellite rainfall estimates. Still, POM presents additional advantages worth noticing: its performance improves as more satellite data becomes available; and POM interpolated rainfall can be directly combined with satellite estimates as forcing for hydrological models leading to minimal “change of support” problems. The use of machine learning models for discharge forecast was developed in four fronts: the comparison of alternative models (e.g. Autoregressive Moving-Average (ARMA), Artificial Neural Networks (ANN) and Support-Vector Regression (SVR)); the enhancement of rainfall aggregation techniques; the study of limitations inherent to SVR forecasting models; and the development of a non-parametric empirical uncertainty post-processor. Going beyond the development of deterministic forecasting models with promising accuracies, even for long lead times of up to 60 days at Victoria Falls, the conducted research most notably motivated a reevaluation of previous v findings reported in literature by showing that SVR models are particularly hazardous when used for discharge forecasting purposes; a conclusion based on their underlying theoretical principles and easily observable in practice. A novel non-parametric empirical uncertainty post-processor was developed. The proposed methodology is able to effectively generate probabilistically correct uncertainties of detrended series given a representative set of training patterns. It is an (informal) technique that, unlike Bayesian methods (formal), does not require the definition of likelihood functions nor an external “conceptual” model of the phenomenon being modeled. Performing, extremely versatile, and straightforward to set up, it can be easily adapted to incorporate new information. The potential range of applicability of the methodology goes well beyond discharge forecasting and even hydrology. The Soil and Water Assessment Tool (SWAT) was used in order to prepare a continuous-time hydrological model of the whole ZRB. Recognizing the importance of sound calibration and validation phases, investments were made on the development of a flexible and computationally efficient calibration interface. In parallel, an analysis of the Soil and Water Assessment Tool (SWAT) hydrological model in its application to the ZRB has evidenced inadequacies in the source code which should be taken into account, particularly in catchments with relatively large waterbodies. Resorting to millions of simulations, the full calibration of daily hydrological models covering the whole basin, from the Upper Zambezi to a few kilometers upstream from the Delta (Marromeu) was accomplished – it is believed – for the first time. Heterogeneity was shown to play a noticeable role in the basin’s hydrology and it is recommended that the calibration of future models allows for the definition of regional parameters. Among four tested calibration schemes, best results were obtained using a regional-regularized calibration approach due to its capacity of approximating contributions, not only of subbasins along the main reach of the Zambezi, but also along its tributaries. The most important outcomes of the research have been, along with original data and works from fellow (African Dams Project) ADAPT researchers, be conveyed to stakeholders through the ongoing ADAPT online database project (http://zambezi.epfl.ch), initially developed in the scope of this thesis. Keywords: artificial neural networks, calibration, discharge forecasting, hydrological modeling, machine learning, optimization, Pattern-Oriented Memory, rainfall interpolation, support-vector machines, SWAT, TRMM, uncertainty, Zambezi. vi Abstract RÉSUMÉ Le bassin du fleuve Zambèze (ZRB), en Afrique, s'étend sur quelques 1 370 000 km2. Dans toute sa splendeur, il abrite environ 30 millions d'habitants, héberge plusieurs sites naturels d'une valeur inestimable et a une potentiel de production hydroélectrique estimé à 13 000 MW, dont seulement environ 5000 MW sont actuellement exploités. Le Zambèze est central à la fois pour la culture et l'économie des pays riverains. Avec un paysage hétérogène et un climat semi-aride, le bassin fait face à de grands défis posés par la croissance démographique, l’enrichissement économique, et le changement climatique. Dans l'avenir, la pression croissante sur les ressources en eau est incontournable. La modélisation hydrologique va certainement aider les parties prenantes à répondre aux défis à venir. Historiquement, la situation politique, la taille et l’hétérogénéité du bassin, ainsi que le manque contraignant de données hydrologiques ont conditionné la précision de modèles hydrologiques du bassin. Les technologies récentes et prometteuses telles que la télédétection par satellite ou l’apprentissage automatique peuvent présenter des alternatives pour résoudre certains de ces problèmes. Le présent travail a entrepris d'élaborer un modèle hydrologique du ZRB à un pas de temps journalier, envisagé pour des utilisations futures telles que des évaluations d'impact environnemental, l'optimisation de l’exploitation et la synchronisation des barrages, ainsi qu’un large éventail d'autres études portant sur des thèmes tels que la faune, de la chimie de l'eau, le transport des sédiments et de la gestion intégrée des eaux. Dans ce but, des problèmes, la plupart liées aux données d'entrée, devront être abordées avant les étapes de modélisation. Le recours aux données de télédétection par satellite est obligatoire et les séries de débits de bonne qualité disponibles devront être prises en compte. En outre, il a été reconnu qu’il n’existe pas de «meilleur» modèle pour un tel éventail d'utilisations et qu'un grand accent devrait être mis sur l'étalonnage du modèle et sa validation. Le Pattern-Oriented Memory (POM), une méthodologie d'interpolation pour précipitations historiques, a été introduit pour prolonger la longueur des simulations. Basé sur les modèles d'apprentissage automatique, le POM est supérieur aux méthodes concurrentes dans des environnements pauvres en données et surtout quand la distribution
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